There exist many problem domains where the interpretability of neural network models is essential for deployment. Here we introduce a recurrent architecture composed of input-switched affine transformations – in other words an RNN without any explicit nonlinearities, but with inputdependent recurrent weights. This simple form allows the RNN to be analyzed via straightforward linear methods: we can exactly characterize the linear contribution of each input to the model predictions; we can use a change-of-basis to disentangle input, output, and computational hidden unit subspaces; we can fully reverse-engineer the architecture’s solution to a simple task. Despite this ease of interpretation, the input switched affine network achieves reasonable performance on a text modeling tasks, and allows greater computational efficiency than networks with standard nonlinearities. --Abstract
The implementation was trained on the Parenthesis task. Here is the result:
Loss as a function of the number of steps
- The paper is available at http://proceedings.mlr.press/v70/foerster17a/foerster17a.pdf
Thanks to Justin Gilmer, one of the authors of the paper for providing some source code under the Apache license.